Dynamic sampling design for characterizing spatiotemporal processes in manufacturing

Chenhui Shao, Jionghua Jin, S. Jack Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Fine-scale characterization and monitoring of spatiotemporal processes are crucial for high-performance quality control of manufacturing processes, such as ultrasonic metal welding and high-precision machining. However, it is generally expensive to acquire high-resolution spatiotemporal data in manufacturing due to the high cost of the 3D measurement system or the time-consuming measurement process. In this paper, we develop a novel dynamic sampling design algorithm to cost-effectively characterize spatiotemporal processes in manufacturing. A spatiotemporal state-space model and Kalman filter are used to pre-dictively determine the measurement locations using a criterion considering both the prediction performance and the measurement cost. The determination of measurement locations is formulated as a binary integer programming problem, and genetic algorithm is applied for searching the optimal design. In addition, a new test statistic is proposed to monitor and update the surface progression rate. Both simulated and real-world spatiotemporal data are used to demonstrate the effectiveness of the proposed method.

Original languageEnglish (US)
Title of host publicationManufacturing Equipment and Systems
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791850749
DOIs
StatePublished - Jan 1 2017
EventASME 2017 12th International Manufacturing Science and Engineering Conference, MSEC 2017 collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing - Los Angeles, United States
Duration: Jun 4 2017Jun 8 2017

Publication series

NameASME 2017 12th International Manufacturing Science and Engineering Conference, MSEC 2017 collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing
Volume3

Other

OtherASME 2017 12th International Manufacturing Science and Engineering Conference, MSEC 2017 collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing
CountryUnited States
CityLos Angeles
Period6/4/176/8/17

Keywords

  • Dynamic sampling design
  • Intelligent sensing
  • Manufacturing
  • Spatiotemporal processes

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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